Although the entropy minimization loss has regularized the deep network at the output level, it also does not consider the image-level information. We hypothesize that the intensity information could bring more useful information and further boost model performance. Here, we attempt to reformate an unsupervised regularization term to consider both prediction and intensity at the same time. Inspired by the clustering learning [1]} and active contour model [2]}, we propose to regularize the deep network by minimizing the intra-class intensity variance, where the mathematical formulation is defined as:
\(\mathcal {L}_{ivm} = \iint (p_c^i \cdot I^i - u_c)^2 didc\) 
